{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1) # set a seed so that the results are consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "   Unnamed: 0         Unnamed: 0.1    X(acc)     Y(acc)    Z(acc)  AGE  \\\n",
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       "2           2  1970-01-01 00:27:30 -0.814029   9.969456 -1.063026   26   \n",
       "3           3  1970-01-01 00:27:30 -0.833182   9.911995 -1.091756   26   \n",
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       "\n",
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       "0     169      64      M     0  1970-01-01 00:27:20 -5.653863  7.945830   \n",
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       "\n",
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      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fall_data= pd.read_csv(\"current_data.csv\")\n",
    "fall_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "       Unnamed: 0         Unnamed: 0.1    X(acc)    Y(acc)    Z(acc)  AGE  \\\n",
       "12688       12688  1970-01-01 00:21:50  8.389283 -0.632069  4.855441   26   \n",
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       "\n",
       "       HEIGHT  WEIGHT GENDER  FALL           Unnamed: 9   X(gyro)   Y(gyro)  \\\n",
       "12688     170      90      F     1  1970-01-01 00:21:40  0.087965  0.093768   \n",
       "12689     170      90      F     1  1970-01-01 00:21:40  0.094073  0.108429   \n",
       "\n",
       "        Z(gyro)          Unnamed: 13    AZIMUTH     PITCH       ROLL  \n",
       "12688 -0.012828  1970-01-01 00:21:40  22.928963  2.164730  61.256130  \n",
       "12689 -0.042150  1970-01-01 00:21:40  22.906473  2.136027  61.233406  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fall_data.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df=fall_data.loc[:,[\"X(acc)\",\"Y(acc)\",\"Z(acc)\",\"X(gyro)\",\"Y(gyro)\",\"Z(gyro)\",\"AZIMUTH\",\"PITCH\",\"ROLL\"]]\n",
    "data_columns=data_df.columns\n",
    "data_columns\n",
    "fig ,axs=plt.subplots(nrows=3,ncols=3,figsize=[10,10])\n",
    "for i in range(0,len(data_columns)):\n",
    "    rows=i//3\n",
    "    cols=i%3\n",
    " \n",
    "   \n",
    "    plot=sns.regplot(x=data_columns[i], y=\"FALL\", data=fall_data, ax=axs[rows,cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x272b8e7b6d8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of the classes\n",
    "fall=fall_data[fall_data[\"FALL\"]==1]\n",
    "notfall=fall_data[fall_data[\"FALL\"]==0]\n",
    "axes=fall.plot(kind='scatter',x='X(acc)',y='AGE',color='red',label='fall')\n",
    "notfall.plot(kind='scatter',x='X(acc)',y='AGE',color='blue',label='notfall',ax=axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_df=fall_data.loc[:,[\"X(acc)\",\"Y(acc)\",\"Z(acc)\",\"X(gyro)\",\"Y(gyro)\",\"Z(gyro)\",\"AZIMUTH\",\"PITCH\",\"ROLL\"]]\n",
    "data_columns=data_df.columns\n",
    "data_columns\n",
    "fig ,axs=plt.subplots(nrows=3,ncols=3,figsize=[10,10])\n",
    "for i in range(0,len(data_columns)):\n",
    "    rows=i//3\n",
    "    cols=i%3\n",
    "    fall=fall_data[fall_data[\"FALL\"]==1]\n",
    "    notfall=fall_data[fall_data[\"FALL\"]==0]\n",
    "    axes=fall.plot(kind='scatter',x=data_columns[i] ,y='AGE',color='red',label='fall',ax=axs[rows,cols])\n",
    "   \n",
    "    notfall.plot(kind='scatter',x=data_columns[i] ,y='AGE',color='blue',label='notfall',ax=axes)\n",
    " \n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#changing object into int where we convert the GENDER as int64 from object\n",
    "fall_data.dtypes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "12685    0\n",
       "12686    0\n",
       "12687    0\n",
       "12688    0\n",
       "12689    0\n",
       "Name: GENDER, Length: 12690, dtype: int32"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder=LabelEncoder()\n",
    "fall_data_gender=fall_data[\"GENDER\"]\n",
    "fall_data_gender_encoded=encoder.fit_transform(fall_data_gender)\n",
    "fall_data['GENDER']=fall_data_gender_encoded\n",
    "fall_data['GENDER']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.7349546e-01,  1.0007763e+01, -5.8418524e-01,  2.6000000e+01,\n",
       "         1.6900000e+02,  6.4000000e+01, -5.6538634e+00,  7.9458300e+00,\n",
       "        -8.4962200e+00,  8.1267914e+01, -9.5822426e+01,  5.3357735e+00,\n",
       "         1.0000000e+00],\n",
       "       [-7.9487497e-01,  9.6246910e+00, -2.9688102e-01,  2.6000000e+01,\n",
       "         1.6900000e+02,  6.4000000e+01, -5.6538634e+00,  3.9461896e-01,\n",
       "        -7.9320850e+00,  8.3388596e+01, -9.5991060e+01,  5.3933450e+00,\n",
       "         1.0000000e+00],\n",
       "       [-8.1402856e-01,  9.9694560e+00, -1.0630256e+00,  2.6000000e+01,\n",
       "         1.6900000e+02,  6.4000000e+01, -5.7549615e+00,  7.4342300e-01,\n",
       "         3.4513887e-02,  8.1035960e+01, -9.5433235e+01,  5.3820133e+00,\n",
       "         1.0000000e+00],\n",
       "       [-8.3318220e-01,  9.9119950e+00, -1.0917560e+00,  2.6000000e+01,\n",
       "         1.6900000e+02,  6.4000000e+01, -1.1676689e+00,  7.5044790e-01,\n",
       "         3.2681290e-02,  8.0905130e+01, -9.3219530e+01,  3.1779182e+00,\n",
       "         1.0000000e+00],\n",
       "       [-8.5233580e-01,  9.8545340e+00, -1.1492168e+00,  2.6000000e+01,\n",
       "         1.6900000e+02,  6.4000000e+01, -1.1725558e+00,  7.6113810e-01,\n",
       "         3.4819320e-02,  8.0744934e+01, -8.6592090e+01,  3.2488322e+00,\n",
       "         1.0000000e+00]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seperating x and y values from data\n",
    "fall_data.columns\n",
    "feature_df=fall_data[['X(acc)', 'Y(acc)', 'Z(acc)', 'AGE','HEIGHT', 'WEIGHT', 'X(gyro)','Y(gyro)', 'Z(gyro)', 'AZIMUTH', 'PITCH', 'ROLL','GENDER']]\n",
    "feature_df\n",
    "#independent var\n",
    "X=np.asarray(feature_df)\n",
    "\n",
    "#dependent var\n",
    "y=np.asarray(fall_data[\"FALL\"])\n",
    "\n",
    "X[0:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10152, 13)\n",
      "(10152,)\n",
      "(2538, 13)\n",
      "(2538,)\n"
     ]
    }
   ],
   "source": [
    "#dividethe data as train/test dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=4)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  the below two cell is not necessary. it is programed just to get some information about y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2534</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2535</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2536</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2537</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2538 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      y_test\n",
       "0          1\n",
       "1          0\n",
       "2          1\n",
       "3          1\n",
       "4          1\n",
       "...      ...\n",
       "2533       0\n",
       "2534       1\n",
       "2535       1\n",
       "2536       1\n",
       "2537       1\n",
       "\n",
       "[2538 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting arrays into datafame to get some information about y_test:\n",
    "df=pd.DataFrame(data=y_test,columns=['y_test'])\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1611\n",
       "0     927\n",
       "Name: y_test, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"y_test\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modelling\n",
    "from sklearn import svm\n",
    "\n",
    "classifier=svm.SVC(gamma=0.1,C=1000)\n",
    "classifier.fit(X_train,y_train)\n",
    "\n",
    "y_predict=classifier.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 924    3]\n",
      " [   0 1611]]\n"
     ]
    }
   ],
   "source": [
    "#result\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "print(metrics.confusion_matrix(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.9988179669030733 \n"
     ]
    }
   ],
   "source": [
    "print ('accuracy = {} '.format(metrics.accuracy_score(y_test,y_predict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       927\n",
      "           1       1.00      1.00      1.00      1611\n",
      "\n",
      "    accuracy                           1.00      2538\n",
      "   macro avg       1.00      1.00      1.00      2538\n",
      "weighted avg       1.00      1.00      1.00      2538\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_predict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.034380708208626445"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "#y_predict=classifier.predict(X_test)\n",
    "fall_mse=mean_squared_error(y_test,y_predict)\n",
    "fall_rmse=np.sqrt(fall_mse)\n",
    "fall_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve,roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "diagnol =[0 for _ in range( len(y_test)) ]\n",
    "auc_diagonal=roc_auc_score(y_test,diagnol)\n",
    "auc_svm=roc_auc_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "svm area under curve = 0.9983818770226538\n",
      "diagonal area under curve = 0.5\n"
     ]
    }
   ],
   "source": [
    "print ('svm area under curve = {}'.format(auc_svm))\n",
    "print ('diagonal area under curve = {}'.format(auc_diagonal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_d_fpr,r_d_tpr,_=roc_curve(y_test,diagnol)\n",
    "r_svm_fpr,r_svm_tpr,_=roc_curve(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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psqOn0nl7wRYub12Xsf2jqRtqReLKG2+eGpqkqjcBv+cxr2yxpiFjvJKSnslnS3dyY6fGhFWtyMwHu1C/utUHKq+8aRqKzjnh7i9o70w4TrP3CIwpTMK2w4yeuootB0/QNKwql0aGWRIo5/JtMBeRJ0QkGbhARI6JSLJ7ej/wVYlFWJysxIQx+TqemsHTX63h2gm/kJaZxaTbrUicv8j3jkBVXwBeEJEXVPWJEozJOdY0ZEy+Rkxcyi9bDnHrJU14tHdLQqxInN/wprP4CRGpCUQClXLMX+BkYM6wpiFjcvrjZBoVgwKpHBzII71bAEL7xjV9HZYpYd50Ft8BPAA0BBKBi4BfcL1pXLaoWh4wxu3b1Xt4+qs1XNOuIU/0bU37xlYgzl9502D+AK5yEttVtQdwIXDA0agcY30Exuw/lsJdk5Zy78fLqV+9MgPanu/rkIyPedMImKKqKSKCiFRU1d9FpKXjkTnB3iMwfm7u7/t4MD6R1IwsxlzZijsubUqQFYnze94kgiQRqQF8CcwWkSPAbmfDcoh1Fhs/F16rCrGNavBM/2ia1bEiccbFm87iQe6PY0VkHlAdmOloVI6xpiHjXzKzlA9/3sbve4/x8pBYmtcNZdLtnXwdlillCkwEIhIArFLVGABVnV8iUTnFmoaMH9m4L5nRn69i+Y4/6NHSisSZ/BWYCFQ1S0RWiki4qu4oqaAcY01Dxg+kZWTxf/M38++5mwipGMj4oW0Z0LaBFYkz+fKmj6A+sFZElgAnsmeqav/CvigifYDXgUDgf6r6Yh7rdAfGAxWAg6razbvQz4a9R2DKv2Mp6by7aCu9o+sxtn80YVUr+jokU8p5kwieOZsNu2sSvQX0ApKABBGZrqrrcqxTA9cIaH1UdYeI1D2bfXnNSkyYciolPZMpCTu56SJXkbhZD3alXjWrEmq8401n8dn2C3QENqnqFgARiQcGAOtyrHMD8EV2s5Oq7j/LfXlHs6xpyJQ7v245xJgvVrP14Ama163KJc3DLAmYInHy8vh8YGeO6ST3vJxaADVF5EcRWSYiN+e1IREZISJLRWTpgQPn+i6bJQJTPiSnpPO3L1cz9O3FZGRl8fEdnbikuRWJM0XnZFWpvM64msf+2+Ma9KYy8IuILFbVDad9SfVt4G2AuLi43NvwnnUWm3JkxMRlLN56iNsvbcojvVtQJdiKxJmz49W/HBGpDISr6voibDsJaJRjuiFnvoiWhKuD+ARwQkQWALHABhxhfQSmbDt8Io3KFVxF4h69oiUi0C7cisSZc1PoWVFErsZVbG6me7qtiEz3YtsJQKSINBWRYGAYkPt7XwFdRCRIRKoAnQDnhsHULMc2bYyTVJXpK3dz+bj5vDbHdZ3UvnFNSwKmWHhzRzAWV8fvjwCqmigiTQr7kqpmiMgoYBaux0ffU9W1InK3e/kEVf1NRGYCq4AsXI+YrjmL4/CONQ2ZMmjv0RT+9uUa5vy2j9iG1RnczorEmeLlTSLIUNWjZ/Myiqp+C3yba96EXNOvAK8UeeNnxZqGTNnyw2+uInHpWVk82bc1t13alMAAu5gxxcubRLBGRG4AAkUkErgf+NnZsBxiJSZMGdO4dgjtGtfkmf7RNAkL8XU4ppzy5vL4PlwD2KcCnwBHgQedDMox1jRkSrnMLOV/C7fwyKcrAWhetyof3tbRkoBxlDd3BC1V9UngSaeDcZ6VmDCl14Z9yTw+dRWJO/+gZ6u6ViTOlBhvEsE4EakPfAbEq+pah2NyjpWYMKVQWkYW//1xM2/O20hopQq8Pqwt/WOtSJwpOd6UmOghIucB1wFvi0g1YIqqPud4dMXNSkyYUuhYSjof/LyVvm3q83S/KGpbkThTwry6PFbVvar6BnA3rncKnnY0KsdY05ApHU6lZfLeT1vJzFJPkbjXh11oScD4RKF3BCLSGhgKDAEOAfHAIw7H5QxrGjKlwM+bDzLm89XsOHySlueFcknzMOpakTjjQ970EbwPTAZ6q2rZHKvYw54aMr5zLCWdF779nclLdtC4dhUm33kRnSNq+zosY7zqI7ioJAIpEWpNQ8Z3RkxcypKth7mrazMevLwFlYPtiSBTOuSbCETkU1W9TkRWc3rVUAFUVS9wPLriZu8RmBJ26HgqVYKDqBwcyON9WhEoQmyjGr4Oy5jTFHRH8ID7z34lEUjJsERgSkZ2kbix09dybVwj/tq3tRWIM6VWvj2nqrrH/fFeVd2e8we4t2TCK2ZWYsKUgD1HT3HHh0t5ID6RxrVDGNK+oa9DMqZA3jxC0yuPeVcWdyAlwpqGjMNmr9tHr3EL+HnzIZ7qF8Xn91xMi3qhvg7LmAIV1EdwD64r/2YisirHolBgkdOBOcM6i42zmoaFENekJs/2jyG8dhVfh2OMVwrqI/gE+A54ARiTY36yqh52NCqn2HsEpphlZGbx3qKt/L4nmXFD29K8blU+uLWjr8MypkgKSgSqqttEZGTuBSJSq0wmAysxYYrRb3uOMfrzVaxKOkqvqHpWJM6UWYXdEfQDlnFmm4oCzRyMyyHWNGTOXWpGJm/N28x/5m2iRpUKvHVDO/q2Oc+KxJkyK99EoKr93H82LblwHGZNQ6YYHE/J4KPF2+kf24Cn+kVRMyTY1yEZc068Gbz+EhEJcX/+i4iME5Fw50NzgDUNmbN0Mi2D/y3cQmaWUttdJG7c0LaWBEy54M3l8X+BkyISCzwObAcmORqVY6xpyBTdok0HuWL8Ap6b8Ru/bjkEQJ1QqxJqyg9vEkGGqiowAHhdVV/H9Qhp2aPYHYHx2tFT6Yyeuoob//crQQEBTBlxERc3D/N1WMYUO2+qjyaLyBPATUAXEQkEKjgbllOsj8B4765JS0nYdoS7u0Xw4OWR9kSQKbe8SQRDgRuA21R1r7t/4BVnw3KIlZgwhTiQnEpIxUCqBAcxuk8rggICaNOwuq/DMsZRhV4eq+pe4GOguoj0A1JUdaLjkTlB1fKAyZOq8sXyJHq9Np/XZm8A4MLwmpYEjF/w5qmh64AlwLW4xi3+VUSGOB2YM6yz2Jxp1x+nuPWDBB7+dCXNwkIY2qGRr0MypkR50zT0JNBBVfcDiEgdYA4w1cnAHGHvEZhcvl+7l4emJKLA2KujuKlzEwID7GLB+BdvEkFAdhJwO4SXg96XOvYegXFTVUSEiLpVuahZbcb2j6ZRLSsSZ/yTN4lgpojMwjVuMbg6j791LiQnWdOQv8vIzOKdhVtZv/cY44ddSESdqrx7Swdfh2WMT3kzZvFjIjIYuBTXWfRtVZ3meGROsKYhv7Zu9zEe/3wla3Yd44poKxJnTDZv7ggAfgYygSwgwblwHGZNQ34pJT2TN+duYsL8zdSoEsx/b2zHlW3q+zosY0oNb54augPXU0ODgCHAYhG5zenAnGFNQ/7oRGoGnyzZwYC25zPn4a6WBIzJxZt2kseAC1X1FlUdDrQHRnuzcRHpIyLrRWSTiIwpYL0OIpLp+GOpNlSl3ziRmsHbCzZ7isTNfqgrr14XS40qViTOmNy8aRpKApJzTCcDOwv7krsUxVu4xjxOAhJEZLqqrstjvZeAWd4Gffasj8AfLNhwgCe+WM3uo6eIOb86F0eEUbuqFYkzJj/eJIJduF4i+wpX28oAYImIPAygquPy+V5HYJOqbgEQkXj3d9flWu8+4HPA+Uc31JqGyrM/Tqbx3IzfmLosiWZ1Qvjsrs7ENanl67CMKfW8SQSb3T/ZvnL/WVgF0vM5/c4hCeiUcwUROR9X30NPCkgEIjICGAEQHn4OQyFY01C5NmLSMpZtP8LIHhHc19OKxBnjLW8eH33mLLed1xlXc02PB0aramZBw/yp6tvA2wBxcXG5t1EEdkdQ3uxPTqFqxSCqBAfx176tqRAoRDew+kDGFIW3j4+ejSQgZ9GWhsDuXOvEAfHuJBAG9BWRDFX90pGI7I6g3FBVpi5L4rkZv3Ft+4b8rV8UbRvV8HVYxpRJTiaCBCBSRJri6mcYhquctUfO8ZBF5APgG8eSANh7BOXEzsMn+eu01SzceJAOTWpyfaeyOXKqMaWFY4lAVTNEZBSup4ECgfdUda2I3O1ePsGpfRcQFdY0VLbNXLOXhz9NRIBnB0Tzl06NCbAiccack0ITgYi0wDVucT1VjRGRC4D+qvpcYd9V1W/JVZcovwSgqrd4FfG5sBITZVZ2kbgW9apySfMw/n51FA1rWpE4Y4qDN2fFd4AngHQAVV2Fq5mn7LGmoTInPTOLt+Zt4oH4RACa1anKOzfHWRIwphh5kwiqqOqSXPMynAjGedY0VJas2XWUAW8u4pVZ68lUJTUj09chGVMuedNHcFBEInA/+ukuA7HH0aicYk8NlQkp6Zm8/sNG3l6whVohwfzfTe25Ivo8X4dlTLnlTSIYiesZ/lYisgvYCvzF0agcY30EZcHJtEw+TdjJNe3O58m+UVSvUsHXIRlTrnnzQtkW4HIRCcE1WllyYd8ptTQLaxoqnY6nZvDR4u3c2aUZtUKCmf1wN2qFWIE4Y0qCN08NPZ1rGgBVfdahmJxlTUOlzo/r9/PktDXsPnqK2IY16BxR25KAMSXIm6ahEzk+VwL6Ab85E46D1F2ZwpqGSo0jJ9L4x4x1fLF8F83rVmXq3RfTvnFNX4dljN/xpmno1ZzTIvIvYLpjETlFs9wf7I6gtLjro2Us336E+3s2Z2TP5lQMsiJxxvjC2bxZXAVoVtyBOM5zR2CJwJf2H0shpGIQIRWDeLJvayoEBhDVoJqvwzLGr3nTR7CaP6uGBgJ1gDLYP5B9CJYIfEFV+WxpEv+YsY7r4hrxVL8oYq1InDGlgjd3BP1yfM4A9qlq2XuhzO4IfGbHIVeRuJ82HaRj01rcaEXijClVCkwEIhIAzFDVmBKKxznZfQSWCErUzDV7eGjKSgIDhOcGxnBDx3ArEmdMKVNgIlDVLBFZKSLhqrqjpIJyhjUNlaTsInEtz6tGtxZ1ePrqKBrUqOzrsIwxefCmaag+sFZElpDjUVJV7e9YVE6wpqESkZaRxf/N38yG/cd5Y1hbmoaFMOGm9r4OyxhTAG8SwdkOVVnK2HsETluV9AePT13F73uTuTq2AWmZWfZIqDFlgDeJoK+qjs45Q0ReAuY7E5JD7D0Cx6SkZ/La7A28s3ALdUIr8s7NcfSKqufrsIwxXvLm8rhXHvOuLO5AHGdNQ445mZbJ1GVJDO3QiO8f6mZJwJgyJt87AhG5B7gXaCYiq3IsCgUWOR1Y8bOmoeKUnJLOpMXbuatrBLVCgpnzcDdqWn0gY8qkgpqGPgG+A14AxuSYn6yqhx2NygnWNFRs5v6+jyenrWHfsRQubFSTzhG1LQkYU4blmwhU9ShwFLi+5MJxkDUNnbNDx1N59pt1fJW4mxb1qvKfGy/mwnArEmdMWXc2tYbKOEsEZ+uej5azYucRHrw8knu7Nyc4yJrZjCkP/CcRWBnqs7L3aAqhlVxF4p7qF0VwUAAtzwv1dVjGmGLkP2dFKzFRJKrK5CU76DVuPuNmbwCgTcPqlgSMKYf8547AU2LCFGb7oROM+Xw1v2w5ROdmtbm5c2Nfh2SMcZD/JALrLPbKt6v38PCniVQICOCFwW0Y1qGRZ3hSY0z55D+JwN4jKFB2kbjW9avRs1VdnuoXRf3qViTOGH/gP2dFe48gT2kZWYyfs4FRk1egqjQNC+E/N7a3JGCMH/GjRGBNQ7kl7vyDq//9E+PnbCQoQEjLzCr8S8aYcseahvzQqbRMxs1ez7s/baVuaCXeHR7HZa2tPpAx/sp/EoE1DXmkpGcybcVuru8YzpgrWxFaqYKvQzLG+JCjl8ci0kdE1ovIJhEZk8fyG0VklfvnZxGJdSwYP28aOpaSzptzN5KRmUXNkGB+eLgb/xzUxpKAMca5OwIRCQTewlXGOglIEJHpqroux2pbgW6qekRErgTeBjo5E5H/DlU5Z90+nvxyNQeSU2nfuBadI2pTvYolAGOMi5NNQx2BTaq6BUBE4oEBgCcRqOrPOdZfDDR0LBo/LDFx6HgqY79ex9crd9PqvFDeuTmOCxrW8HVYxphSxslEcD6wM8d0EgVf7d+Oq+z1GURkBDACIDw8/Oyi8cMSE9lF4p1vD4EAAB72SURBVB7u1YK7u0VYkThjTJ6cTAR5nXHzrPMgIj1wJYJL81quqm/jajYiLi7uHGtFlO9EsOfoKapVqkBIxSCevtpVJK5FPasPZIzJn5OXiElAoxzTDYHduVcSkQuA/wEDVPWQY9GU887irCzl41+302vcAl793lUkLub86pYEjDGFcvKOIAGIFJGmwC5gGHBDzhVEJBz4ArhJVTc4GAvl+T2CrQdPMObzVfy69TCXNK/NLRc38XVIxpgyxLFEoKoZIjIKmAUEAu+p6loRudu9fALwNFAb+I+7sFmGqsY5E1D5fGt2xipXkbjgoABevuYCro1raEXijDFF4ugLZar6LfBtrnkTcny+A7jDyRhy7Nj1Zzk5SWYXiYtuUI1eUfV4ql8U9apV8nVYxpgyqPy1k+SrfDQNpWZkMu779Yz8ZDmqSpOwEN68oZ0lAWPMWSvbZ8WiKAclJpbvOEK/N37ijbmbqBQUaEXijDHFwo9qDZXdpqGTaRn8a9YG3v95K/WrVeL9WzvQo2VdX4dljCkn/CcRlOESE6npWXy9ajc3XdSYx/u0ompFP/prM8Y4zn/OKGWsxMTRU+l8+PM27u0eQc2QYOY83I3qla0+kDGm+PlRIig7JSZmrd3LU1+u4dCJNDo1rUWnZrUtCRhjHOM/iaAMNA0dSE5l7PS1zFi9h9b1q/Hu8A60aVjd12GZYpCenk5SUhIpKSm+DsWUc5UqVaJhw4ZUqOD9xaP/JIIy0Fl878fLWLnzKI/2bsFd3SKoEFg2mrFM4ZKSkggNDaVJkyb2wp9xjKpy6NAhkpKSaNq0qdff88NEULpOrrv+OEX1yhWoWjGIv18dTcWgACKtPlC5k5KSYknAOE5EqF27NgcOHCjS90rXWdFRpatpKCtLmfjLNnqPm8+4HEXiLAmUX5YETEk4m39nfnhH4Pv/GTcfOM6Yz1eRsO0IXSLDuPWSJr4OyRjjx/zvjsDHieCbVbu58vWFrN+bzCtDLmDibR1pVKuKT2My/mns2LH861//4umnn2bOnDkltt8mTZpw8OBBAE6dOkW3bt3IzMz0LH/ttdeoVKkSR48e9cz74IMPGDVq1Gnb6d69O0uXLvVss02bNlxwwQV069aN7du3e9ZLSkpiwIABREZGEhERwQMPPEBaWppn+ZIlS+jatSstW7akVatW3HHHHZw8edKrY5k5cyYtW7akefPmvPjii3mu8+OPP1K9enXatm1L27ZtefbZZwv9/uHDh+nVqxeRkZH06tWLI0eOALB69WpuueUWr2IrCv9JBD4uMaHuO5I251enT/R5zHmkG9fGNbLmAuNzzz77LJdffrlP9v3ee+8xePBgAgMDPfMmT55Mhw4dmDZtWpG2NW/ePFatWkX37t157rnnANf/d4MHD2bgwIFs3LiRDRs2cPz4cZ588kkA9u3bx7XXXstLL73E+vXr+e233+jTpw/JycmF7i8zM5ORI0fy3XffsW7dOiZPnsy6devyXLdLly4kJiaSmJjI008/Xej3X3zxRS677DI2btzIZZdd5kkSbdq0ISkpiR07dhTpd1MYP0oEvrkjSEnP5JVZv3PPR64icY1rh/DG9RdSN9SKxPmzof/3yxk/k37ZBsCptMw8l3+21DXy6+ETaWcs89Y///lPWrZsyeWXX8769esBuOWWW5g6dSrgSgodOnQgJiaGESNGeC5gEhISuOCCC+jcuTOPPfYYMTExgOtKffDgwfTp04fIyEgef/xxz74mT55MmzZtiImJYfTo0XnG8/HHHzNgwADP9ObNmzl+/DjPPfcckydP9vq4curcuTO7du0CYO7cuVSqVIlbb70VgMDAQF577TXee+89Tp48yVtvvcXw4cPp3Lkz4GpfHzJkCPXq1St0P0uWLKF58+Y0a9aM4OBghg0bxldffeV1nAV9/6uvvmL48OEADB8+nC+//NLzvauvvpr4+Hiv9+MN/0kEPugsXrb9MFe9sZC35m0mpGKQFYkzPrVs2TLi4+NZsWIFX3zxBQkJCWesM2rUKBISElizZg2nTp3im2++AeDWW29lwoQJ/PLLL6ddvQMkJiYyZcoUVq9ezZQpU9i5cye7d+9m9OjRzJ07l8TERBISEk47mQGkpaWxZcsWmjRp4pk3efJkrr/+erp06cL69evZv39/kY9z5syZDBw4EIC1a9fSvn3705ZXq1aN8PBwNm3axJo1a85Ynm3evHme5pycPxdffDEAu3btolGjPwdhbNiwoScB5fbLL78QGxvLlVdeydq1awv9/r59+6hfvz4A9evXP+33EBcXx8KFC4v0OymMH3YWO5/7TqRm8Mqs9Xz4yzYaVK/Mh7d1pFuLOo7v15QdU+7qnO+yysGBBS6vFRJc4PL8LFy4kEGDBlGliqtPqn///mesM2/ePF5++WVOnjzJ4cOHiY6OpkuXLiQnJ3tOgDfccIMnQQBcdtllVK/uevExKiqK7du3c+jQIbp3706dOq5/9zfeeCMLFizwnKABDh48SI0aNU7bf3x8PNOmTSMgIIDBgwfz2WefMXLkyHybUHPO79GjB/v27aNu3bqnNQ3l9d385ufUo0cPEhMT812efbeUXzzZ2rVrx/bt26latSrffvutp5nK2+/nVrduXXbvPmPU33PiP3cEJVhiIj0zi29X7+Hmixoz66GulgRMqVHQiSYlJYV7772XqVOnsnr1au68805SUlLyPGHlVLFiRc/nwMBAMjIyCv0OQOXKlU9703rVqlVs3LiRXr160aRJE+Lj4z3NQ7Vr1/Z0mGY7fPgwYWFhnul58+axfft2oqOjPe3w0dHRng7lbMeOHWPnzp1EREQQHR3NsmXL8oyvsDuChg0bsnPnTs/6SUlJNGjQ4IztVKtWjapVqwLQt29f0tPTOXjwYIHfr1evHnv27AFgz5491K37Z7XhlJQUKleunGfMZ8t/EoHDTUN/nEzjtdkbyMjMokaVYOY80o1nBsRYpVBTanTt2pVp06Zx6tQpkpOT+frrr09bnn1SDgsL4/jx455+g5o1axIaGsrixYsBvGqf7tSpE/Pnz+fgwYNkZmYyefJkunXrdto6NWvWJDMz07PfyZMnM3bsWLZt28a2bdvYvXs3u3btYvv27XTo0IFFixaxd+9eAJYuXUpqauppTSvgSi7jx49n4sSJHD58mMsuu4yTJ08yceJEwNVB+8gjj3DLLbdQpUoVRo0axYcffsivv/7q2cZHH33E3r17PXcEuX9+/vlnADp06MDGjRvZunUraWlpxMfH53mXtXfvXk9iXLJkCVlZWdSuXbvA7/fv358PP/wQgA8//PC0fpQNGzZ4+miKi/8kAgebhr5bvYfLxy3gzXmbWLbdddVSrZIViTOlS7t27Rg6dCht27blmmuuoUuXLqctr1GjBnfeeSdt2rRh4MCBdOjQwbPs3XffZcSIEXTu3BlV9TQF5ad+/fq88MIL9OjRg9jYWNq1a3faySxb7969+emnnwBXghk0aNBpywcNGkR8fDz16tXj9ddfp2/fvrRt25YHH3yQyZMnExBw5v/P9evX5/rrr+ett95CRJg2bRqfffYZkZGRtGjRgkqVKvH8888Drivv+Ph4Hn30UVq2bEnr1q1ZuHAh1apVK/T3GRQUxJtvvskVV1xB69atue6664iOjgZgwoQJTJjgGpV36tSpxMTEEBsby/333098fDwiUuD3x4wZw+zZs4mMjGT27NmMGTPGs9958+Zx1VVXFRpfUYg3t3ClSVxcnOa+1fPK5nkwaSDc+h00vrhYYtl/LIWnv1rLzLV7iW5QjZeHXEB0AysSZ87022+/0bp1a1+HcdaOHz/uad548cUX2bNnD6+//vo5b3fFihWMGzeOSZMmnfO2/EFqairdunXjp59+Iigo/9aGvP69icgyVY3La30/arco/qahkZ8sZ2XSUUb3acWdXZoSZEXiTDk1Y8YMXnjhBTIyMmjcuDEffPBBsWz3wgsvpEePHmRmZp7xNJI5044dO3jxxRcLTAJnw38SQTG9R5B05CQ1qgRTtWIQY/tHU6lCIBF1qhZDgMaUXkOHDmXo0KGObPu2225zZLvlUWRkJJGRkcW+XT+6hD23PoKsLOWDRVvp/doCXv3e9SJOdIPqlgSMMWWe/90RnEXT0Kb9riJxS7cfoVuLOtx+qfd1vo0xprTzv0RQxKah6St38+inK6lSMZBx18Uy6MLzrT6QMaZc8Z9EUMTO4qwsJSBAiG1Ynb5tzuPJq6KoE1qx8C8aY0wZ4z99BF7eEaSkZ/Lid79z90fLPEXixg+70JKAMQ4bMmQIW7Zs8UyvWLECEWHWrFmeedu2bTvjZarsctrgKqDXtGlT2rZtS2xsLD/88INnvbS0NB588EEiIiKIjIxkwIABJCUleZbv3buXYcOGERERQVRUFH379mXDhg1exb5161Y6depEZGQkQ4cOPa3MdU6jR48mJiaGmJgYpkyZ4pk/d+5c2rVrR0xMDMOHDycjIwOAo0ePcvXVVxMbG0t0dDTvv/++51i6du3qWe9c+VEiKLzExJKth+n7+kImzN9MzSrBpGeWrXcsTDmzcwksfNX1ZxmSc2wBb61du5bMzEyaNWvmmTd58mQuvfTSIlchfeWVV0hMTGT8+PHcfffdnvl//etfSU5OZsOGDWzcuJGBAwcyePBgVBVVZdCgQXTv3p3Nmzezbt06nn/+efbt2+fVPkePHs1DDz3Exo0bqVmzJu++++4Z68yYMYPly5eTmJjIr7/+yiuvvMKxY8fIyspi+PDhxMfHs2bNGho3bux5q/itt94iKiqKlStX8uOPP/LII4+QlpZGcHAwl1122WnJ5FxY0xBwPDWDl777nUmLt9OoVmU+ur0Tl0aGnbGeMcXiuzGwd3XB66Qeg31rXBcwEgD1YqBiAW+7ntcGrsx7YBSAEydOcN1115GUlERmZiZPPfUU1apV4/333+fTTz8FXAOovPrqq3z99ddUrVqVkSNHMmfOHGrWrMnzzz/P448/zo4dOxg/fvwZpRR+/PFHnnnmGerXr09iYiLLly/nnnvuYenSpQQFBTFu3DjP+wKjR49m1qxZiAh33nkn99133xnlqFWVqVOnMnv2bLp06UJKSgqVKhWtdHvOctQnT57k/fffZ+vWrZ73FW699Vbee+895s6di4hQoUKF0xJH27ZtvdqPqjJ37lw++eQTwFU2euzYsdxzzz2nrbdu3Tq6detGUFAQQUFBxMbGMnPmTHr06EHFihVp0aIFAL169eKFF17g9ttvR0RITk5GVTl+/Di1atXyvEMwcOBAnnjiCW688cYi/V7y4j+JoIASExmZWXy/bi+3XdKUR69oQZVg//m1mFIq5eifd7Ga5ZouKBEUYubMmTRo0IAZM2YAriaHkJAQ7rrrLk6cOEFISAhTpkzxvCtw4sQJunfvzksvvcSgQYP429/+xuzZs1m3bh3Dhw/Ps6bOkiVLWLNmDU2bNuXVV18FXCNq/f777/Tu3ZsNGzZ4TsYrVqwgKCiIw4cPA7Bo0SKuv/56z7YWLVpE06ZNiYiIoHv37nz77bcMHjy4yMecXe1006ZNhIeHn1E6Ii4uzlMWOr9y1MnJyWeU48j2ySefULduXWrUqOE5QedXjjo2NpZnnnmGhx9+mJMnTzJv3jyioqIICwsjPT2dpUuXEhcXx9SpUz3F6EaNGkX//v1p0KABycnJTJkyxVNWIyYmJs9S4mfDf854uZqGjpxI4/1FW7n/skhqVAnmh0e6W4E4UzIKuHL32LkEPuwPmWkQGAzX/A8adTzrXbZp04ZHH32U0aNH069fP8+JrU+fPnz99dcMGTKEGTNm8PLLLwMQHBxMnz59PN+tWLEiFSpUoE2bNmzbti3PfXTs2JGmTV2PVv/000/cd999ALRq1YrGjRuzYcMG5syZw9133+05adaqVQtwVdjMLlkNrmahYcOGATBs2DAmTZrE4MGDvSpH/dhjj/H444+zf/9+T6G8wspRF1RqJzQ0tMBy1AcOHCgwnmy9e/cmISGBiy++mDp16tC5c2eCgoIQEeLj43nooYdITU2ld+/ent/PrFmzaNu2LXPnzmXz5s306tWLLl26UK1aNQIDAwkODiY5OZnQ0NB84/OGo30EItJHRNaLyCYRGZPHchGRN9zLV4lIO+eiUc9/Z6zaQ6/X5vOfHzezfMcfAJYETOnSqCMMnw49n3T9eQ5JAKBFixYsW7aMNm3a8MQTT3jGzR06dCiffvopc+fOpUOHDp4TSoUKFTwns4CAAE+p6YCAgHw7KENCQjyf8zux5ndCzlmSOjMzk88//5xnn32WJk2acN999/Hdd9+RnJzsVTnqV155hU2bNvHcc895Rvlq3rw527dvP2MIyuXLlxMVFVVgOerk5OQ8y1G3bduWdevWERYWxh9//OH5veRXjhrgySefJDExkdmzZ6OqnreEO3fuzMKFCz3jJ2fPf//99z0JsHnz5jRt2pTff//ds73U1NQiN5nlxbFEICKBwFvAlUAUcL2IROVa7Uog0v0zAvivU/FwcCMA733+DSM/WU796pWZPupSOjat5dgujTknjTpCl0fOOQkA7N69mypVqvCXv/yFRx99lOXLlwOuAeCXL1/OO++8U6wlJLp27crHH38MuMom79ixg5YtW9K7d28mTJjgOWlmNw21bt2aTZs2ATBnzhxiY2PZuXMn27ZtY/v27VxzzTV8+eWXVK1alfr163ueBjp8+DAzZ87k0ksvPW3/AQEBPPDAA2RlZTFr1ixCQkIYPnw4Dz/8sKcze+LEiZw8eZKePXvSs2dPUlNTeeeddzzbSEhIYP78+Z47grx+oqKiEBF69OjhKdudu2x0tszMTA4dOgS4xl5YtWoVvXv3BvCMQJaamspLL73k6asIDw/3HOu+fftYv369p0P90KFD1KlThwoViqHScXaPeXH/AJ2BWTmmnwCeyLXO/wHX55heD9QvaLvt27fXItvxq+qzYap/r6annq6t0776XNMzMou+HWPO0rp163y6/5kzZ2qbNm00NjZW4+LiNCEhwbNs5MiRGhISoidOnPDMCwkJ8Xz++9//rq+88kqey7LNmzdPr7rqKs/0qVOndPjw4RoTE6Nt27bVuXPnqqpqenq6PvTQQ9q6dWu94IIL9N///reqqk6cOFGffPJJVVUdPny4/ve//z1t+1999ZX26dNHVVXXrl2r3bt319jYWI2NjdWPPvrIs97w4cP1s88+80xPnTpVe/bsqaqqKSkpOmrUKG3WrJk2b95c+/Xrpzt27PCsu2vXLr322mu1WbNmGhUVpX379tUNGzYU+rtVVd28ebN26NBBIyIidMiQIZqSkqKqqgkJCXr77bd7fietW7fW1q1ba6dOnXTFihWe7z/66KPaqlUrbdGihb722munxdSrVy+NiYnR6OhonTRpkmfZZ599pg8//HCe8eT17w1YqvmcVx0rQy0iQ4A+qnqHe/omoJOqjsqxzjfAi6r6k3v6B2C0qi7Nta0RuO4YCA8Pb799+/aiBbPwVZj7HGgWKoFIzyddV1rGlJCyXobaaadOnaJHjx4sWrTIqpB6afDgwbzwwgu0bNnyjGVFLUPtZB9BXr06ubOON+ugqm+rapyqxuXsUPJaky4QWBEkEAkMdk0bY0qNypUr88wzz+Q7+Ls5XVpaGgMHDswzCZwNJ3tIk4Cc48g1BHKPuOzNOucuu+Nt20JXEiiGNldjTPG64oorfB1CmREcHMzNN99cbNtzMhEkAJEi0hTYBQwDbsi1znRglIjEA52Ao6q6x5FoGnW0BGB8SvN5YsaY4nQ2zf2OJQJVzRCRUcAsIBB4T1XXisjd7uUTgG+BvsAm4CRwq1PxGONLlSpV4tChQ9SuXduSgXGMqnLo0KEiP1LqP2MWG+ND6enpJCUleZ6VN8YplSpVomHDhmc8VmpjFhvjYxUqVPC8dWtMaeM/1UeNMcbkyRKBMcb4OUsExhjj58pcZ7GIHACK+GqxRxhwsBjDKQvsmP2DHbN/OJdjbqyqeb6RW+YSwbkQkaX59ZqXV3bM/sGO2T84dczWNGSMMX7OEoExxvg5f0sEb/s6AB+wY/YPdsz+wZFj9qs+AmOMMWfytzsCY4wxuVgiMMYYP1cuE4GI9BGR9SKySUTG5LFcROQN9/JVItLOF3EWJy+O+Ub3sa4SkZ9FJNYXcRanwo45x3odRCTTPWpemebNMYtIdxFJFJG1IjK/pGMsbl78264uIl+LyEr3MZfpKsYi8p6I7BeRNfksL/7zV35jWJbVH1wlrzcDzYBgYCUQlWudvsB3uEZIuwj41ddxl8AxXwzUdH++0h+OOcd6c3GVPB/i67hL4O+5BrAOCHdP1/V13CVwzH8FXnJ/rgMcBoJ9Hfs5HHNXoB2wJp/lxX7+Ko93BB2BTaq6RVXTgHhgQK51BgAT1WUxUENE6pd0oMWo0GNW1Z9V9Yh7cjGu0eDKMm/+ngHuAz4H9pdkcA7x5phvAL5Q1R0AqlrWj9ubY1YgVFwDPVTFlQgySjbM4qOqC3AdQ36K/fxVHhPB+cDOHNNJ7nlFXacsKerx3I7riqIsK/SYReR8YBAwoQTjcpI3f88tgJoi8qOILBOR4hvP0De8OeY3gda4hrldDTygqlklE55PFPv5qzyOR5DX8E+5n5H1Zp2yxOvjEZEeuBLBpY5G5Dxvjnk8MFpVM8vJqGDeHHMQ0B64DKgM/CIii1V1g9PBOcSbY74CSAR6AhHAbBFZqKrHnA7OR4r9/FUeE0ES0CjHdENcVwpFXacs8ep4ROQC4H/Alap6qIRic4o3xxwHxLuTQBjQV0QyVPXLkgmx2Hn7b/ugqp4ATojIAiAWKKuJwJtjvhV4UV0N6JtEZCvQClhSMiGWuGI/f5XHpqEEIFJEmopIMDAMmJ5rnenAze7e94uAo6q6p6QDLUaFHrOIhANfADeV4avDnAo9ZlVtqqpNVLUJMBW4twwnAfDu3/ZXQBcRCRKRKkAn4LcSjrM4eXPMO3DdASEi9YCWwJYSjbJkFfv5q9zdEahqhoiMAmbheuLgPVVdKyJ3u5dPwPUESV9gE3AS1xVFmeXlMT8N1Ab+475CztAyXLnRy2MuV7w5ZlX9TURmAquALOB/qprnY4hlgZd/z/8APhCR1biaTUarapktTy0ik4HuQJiIJAF/ByqAc+cvKzFhjDF+rjw2DRljjCkCSwTGGOPnLBEYY4yfs0RgjDF+zhKBMcb4OUsEplQTkftF5DcR+biAdbqLyDclGVd+RKR/doVMERkoIlE5lj0rIpeXYCzdReTiktqfKbvK3XsEpty5F9eb0Ft9HYg3VHU6f77wNBD4Blc1UFT16eLen4gEqWp+Bda6A8eBn4t7v6Z8sTsCU2qJyARc5Yeni8hDItLRPZbCCvefLfP4Tjd3Lf5E93qh7vmPiUiCu377M/ns77iIvCoiy0XkBxGp457fVkQWu787TURquuffLyLr3PPj3fNuEZE33Vfi/YFX3LFEiMgHIjJERK4UkU9z7Le7iHzt/txbRH5xx/CZiFTNI84fReR5cY018ICIXC0iv7qPd46I1BORJsDdwEPu/XcRkToi8rn795AgIpecw1+PKU98XXvbfuynoB9gGxDm/lwNCHJ/vhz43P25O/CN+/PXwCXuz1Vx3fX2xjXot+C6+PkG6JrHvhS40f35aeBN9+dVQDf352eB8e7Pu4GK7s813H/ekuN7H5BjDITsaXdMO4AQ9/z/An/BVQ9pQY75o4Gn84jzR+A/OaZr8ufLoXcAr7o/jwUezbHeJ8Cl7s/hwG++/vu1n9LxY01DpiypDnwoIpG4TtoV8lhnETDO3afwhaomiUhvXMlghXudqkAkrpNuTlnAFPfnj4AvRKQ6rpN89khfHwKfuT+vAj4WkS8Br2sYqatswkzgahGZClwFPA50A6KARe4yIMHAL/lsZkqOzw2BKeKqSR8M5NeMdjkQJX9WYq0mIqGqmuxt7KZ8skRgypJ/APNUdZC76ePH3Cuo6osiMgNXLZbF7s5ZAV5Q1f8r4v4Kq79yFa7RpPoDT4lIdBG2PQUYiWsAkgRVTRbXGXq2ql7vxfdP5Pj8b2Ccqk4Xke647gTyEgB0VtVTRYjT+AHrIzBlSXVgl/vzLXmtICIRqrpaVV8CluIqRzwLuC27vV1EzheRunl8PQBX0w24Rvr6SVWPAkdEpIt7/k3AfBEJABqp6jxcV/M1cN1p5JQMhOZzLD/iGo7wTv68ul8MXCIizd1xVhGRFvl8P6ecv5fhBez/e2BU9oSItPVi28YPWCIwZcnLwAsisghXJcq8PCgia0RkJXAK+E5Vv8fVPv6Lu0LlVPI+QZ8AokVkGa5BTp51zx+Oq9N3FdDWPT8Q+Mi9vRXAa6r6R67txQOPuTtxI3IuUNVMXH0VV7r/RFUP4Epwk937WowrkRVmLPCZiCwEclbd/BoYlN1ZDNwPxLk7t9fh6kw2xqqPGpNNRI6r6hlP6RhT3tkdgTHG+Dm7IzDGGD9ndwTGGOPnLBEYY4yfs0RgjDF+zhKBMcb4OUsExhjj5/4fGXhYxAOOsKEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r_d_fpr,r_d_tpr,linestyle='--',label='diagnol(AUROC=%0.3f)'%auc_diagonal)\n",
    "plt.plot(r_svm_fpr,r_svm_tpr,marker='.',label='svm roc(AUROC=%0.3f)'%auc_svm)\n",
    "plt.title('ROC plot')\n",
    "plt.xlabel('false positive rate')\n",
    "plt.ylabel('true positive rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python 3.6 (tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
